docs: def calc_bca_interval(self, conf_percentage):
          """
          Calculates Bias-Corrected and Accelerated (BCa) Bootstrap Confidence
          Intervals for one's model.
  
          Parameters
          ----------
          conf_percentage : scalar in the interval (0.0, 100.0).
              Denotes the confidence-level for the returned endpoints. For
              instance, to calculate a 95% confidence interval, pass `95`.
  
          Returns
          -------
          None. Will store the BCa intervals as `self.abc_interval`.
  
          Notes
          -----
          Must have all ready called `self.generate_bootstrap_replicates` and
          `self.generate_jackknife_replicates`.
          """
          # Get the alpha % that corresponds to the given confidence percentage.
          alpha = bc.get_alpha_from_conf_percentage(conf_percentage)
          # Create the column names for the dataframe of confidence intervals
          single_column_names =\
              ['{:.3g}%'.format(alpha / 2.0),
               '{:.3g}%'.format(100 - alpha / 2.0)]
          # Bundle the arguments needed to create the desired confidence interval
          args = [self.bootstrap_replicates.values,
                  self.jackknife_replicates.values,
                  self.mle_params.values,
                  conf_percentage]
          # Calculate the BCa confidence intervals.
          conf_intervals = bc.calc_bca_interval(*args)
          # Store the BCa confidence intervals.
          self.bca_interval = pd.DataFrame(conf_intervals.T,
                                           index=self.mle_params.index,
                                           columns=single_column_names)
          return None